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Supply Chain Adjustments to Tariff Shocks: Evidence from Firm Trade Linkages in the 2018-2019 U.S. Trade War
August 2024
Working Paper Number:
CES-24-43
We use the 2018-2019 U.S. trade war to examine how supply chains adjustments to a tariff cost shock affect imports and exports. Using confidential firm-trade linked data, we show that the decline in imports of tariffed goods was driven by discontinuations of U.S. buyer'foreign supplier relationships, reduced formation of new relationships, and exits by U.S. firms from import markets altogether. However, tariffed products where imports were concentrated in fewer suppliers had a smaller decline in import growth. We then construct measures of export exposure to import tariffs by linking tariffs paid by importing firms to their exported products. We find that the most exposed products had lower exports in 2018-2019, with most of the impact occurring in 2019.
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Competition, Firm Innovation, and Growth under Imperfect Technology Spillovers
July 2024
Working Paper Number:
CES-24-40
We study how friction in learning others' technology, termed 'imperfect technology spillovers,' incentivizes firms to use different types of innovation and impacts the implications of competition through changes in innovation composition. We build an endogenous growth model in which multi-product firms enhance their products via internal innovation and enter new product markets through external innovation. When learning others' technology takes time due to this friction, increased competitive pressure leads firms with technological advantages to intensify internal innovation to protect their markets, thereby reducing others' external innovation. Using the U.S. administrative firm-level data, we provide regression results supporting the model predictions. Our findings highlight the importance of strategic firm innovation choices and changes in their composition in shaping the aggregate implications of competition.
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Starting Up AI
March 2024
Working Paper Number:
CES-24-09
Using comprehensive administrative data on business applications over the period 2004-2023, we study emerging business ideas for developing AI technologies or producing goods or services that use, integrate, or rely on AI. The annual number of new AI business applications is stable between 2004 and 2012 but begins to rise after 2012, and increases faster from 2016 onward into the pandemic, with a large, discrete jump in 2023. The distribution of AI business applications is highly uneven across states and sectors. AI business applications have a higher likelihood of becoming employer startups and higher expected initial employment compared to other business applications. Moreover, controlling for application characteristics, employer businesses originating from AI business applications exhibit higher employment, revenue, payroll, average pay per employee, and labor share, but have similar labor productivity and lower survival rate, compared to those originating from other business applications. While these early patterns may change as the diffusion of AI progresses, the rapid rise in AI business applications, combined with their generally higher rate of transition to employers and better performance in some post-transition outcomes, suggests a small but growing contribution from these applications to business dynamism.
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Low-Wage Jobs, Foreign-Born Workers, and Firm Performance
January 2024
Working Paper Number:
CES-24-05
We examine how migrant workers impact firm performance using administrative data from the United States. Exploiting an unexpected change in firms' likelihood of securing low-wage workers through the H-2B visa program, we find limited crowd-out of other forms of employment and no impact on average pay at the firm. Yet, access to H-2B workers raises firms' annual revenues and survival likelihood. Our results are consistent with the notion that guest worker programs can help address labor shortages without inflicting large losses on incumbent workers.
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Outsourcing Dynamism
December 2023
Working Paper Number:
CES-23-64
This paper investigates the increasing importance of domestic outsourcing in U.S. manufacturing. Under domestic outsourcing, the agency is the employer of record for temporary workers, though they perform their tasks at the client business' premises. On a yearly basis, one in two manufacturing plants hires at least some of its workers through a temporary help agency. Furthermore, domestic outsourcing is becoming increasingly more important: the average share of revenue spent on such arrangements has gone up by 85 percent since 2006. We develop a methodology to transform reported expenses on temporary and leased workers into plant-level outsourced employment counts, using administrative data on the U.S. manufacturing sector. We find that domestic outsourcing is an important margin of adjustment that plants use to modify their workforce in response to productivity shocks. Plant-level outsourced employment adjusts more quickly and is twice as responsive as payroll employment. These micro implications have significant aggregate consequences. Without taking reallocations in outsourced employment into account, the measured pace at which jobs reallocate across workplaces is underestimated. On average, we omit the equivalent of 15 percent of payroll employment reallocations in each year. However, outsourced employment churns at a much higher rate compared to its payroll counterpart. Therefore, the omission of outsourced reallocations can rationalize 37 percent of the secular decline in the aggregate job reallocation rate. Lastly, the extent of mismeasurement varies with the business cycle; falling in downturns and increasing in upturns implying that the speed of economic recovery is underestimated.
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Unionization, Employer Opposition, and Establishment Closure
July 2023
Working Paper Number:
CES-23-35
We study the effect of private-sector unionization on establishment employment and survival. Specifically, we analyze National Labor Relations Board union elections from 1981'2005 using administrative Census data. Our empirical strategy extends standard difference-in-differences techniques with regression discontinuity extrapolation methods. This allows us to avoid biases from only comparing close elections and to estimate treatment effects that include larger marginof- victory elections. Using this strategy, we show that unionization decreases an establishment's employment and likelihood of survival, particularly in manufacturing and other blue-collar and industrial sectors. We hypothesize that two reasons for these effects are firms' ability to avoid working with new unions and employers' opposition to unions. We find that the negative effects are significantly larger for elections at multi-establishment firms. Additionally, after a successful union election at one establishment, employment increases at the firms' other establishments. Both pieces of evidence are consistent with firms avoiding new unions by shifting production from unionized establishments to other establishments. Finally, we find larger declines in employment and survival following elections where managers or owners were likely more opposed to the union. This evidence supports new reasons for the negative effects of unionization we document.
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The Demographics of the Recipients of the First Economic Impact Payment
May 2023
Working Paper Number:
CES-23-24
Starting in April 2020, the federal government began to distribute Economic Impact Payments (EIPs) in response to the health and economic crisis caused by COVID-19. More than 160 million payments were disbursed. We produce statistics concerning the receipt of EIPs by individuals and households across key demographic subgroups. We find that payments went out particularly quickly to households with children and lower-income households, and the rate of receipt was quite high for individuals over age 60, likely due to a coordinated effort to issue payments automatically to Social Security recipients. We disaggregate statistics by race/ethnicity to document whether racial disparities arose in EIP disbursement. Receipt rates were high overall, with limited differences across racial/ethnic subgroups. We provide a set of detailed counts in tables for use by the public.
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Estimating the U.S. Citizen Voting-Age Population (CVAP) Using Blended Survey Data, Administrative Record Data, and Modeling: Technical Report
April 2023
Authors:
J. David Brown,
Danielle H. Sandler,
Lawrence Warren,
Moises Yi,
Misty L. Heggeness,
Joseph L. Schafer,
Matthew Spence,
Marta Murray-Close,
Carl Lieberman,
Genevieve Denoeux,
Lauren Medina
Working Paper Number:
CES-23-21
This report develops a method using administrative records (AR) to fill in responses for nonresponding American Community Survey (ACS) housing units rather than adjusting survey weights to account for selection of a subset of nonresponding housing units for follow-up interviews and for nonresponse bias. The method also inserts AR and modeling in place of edits and imputations for ACS survey citizenship item nonresponses. We produce Citizen Voting-Age Population (CVAP) tabulations using this enhanced CVAP method and compare them to published estimates. The enhanced CVAP method produces a 0.74 percentage point lower citizen share, and it is 3.05 percentage points lower for voting-age Hispanics. The latter result can be partly explained by omissions of voting-age Hispanic noncitizens with unknown legal status from ACS household responses. Weight adjustments may be less effective at addressing nonresponse bias under those conditions.
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Federal-Local Partnerships on Immigration Law Enforcement: Are the Policies Effective in Reducing Violent Victimization?
April 2023
Working Paper Number:
CES-23-18
Our understanding of how immigration enforcement impacts crime has been informed by data from the police crime statistics. This study complements existing research by using longitudinal multilevel data from the National Crime Victimization Survey (NCVS) for 2005-2014 to simultaneously assess the impact of the three predominant immigration policies that have been implemented in local communities. The results indicate that the activation of Secure Communities and 287(g) task force agreements significantly increased violent victimization risk among Latinos, whereas they showed no evident impact on victimization risk among non-Latino Whites and Blacks. The activation of 287(g) jail enforcement agreements and anti-detainer policies had no significant impact on violent victimization risk during the period.Contrary to their stated purpose of enhancing public safety, our results show that the Secure Communities program and 287(g) task force agreements did not reduce crime, but instead eroded security in American communities by increasing the likelihood that Latinos experienced violent victimization. These results support the Federal government's ending of 287(g) task force agreements and its more recent move to end the Secure Communities program. Additionally, the results of our study add to the evidence challenging claims that anti-detainer policies pose a threat to violence risk.
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Business Dynamics Statistics for Single-Unit Firms
December 2022
Working Paper Number:
CES-22-57
The Business Dynamics Statistics of Single Unit Firms (BDS-SU) is an experimental data product that provides information on employment and payroll dynamics for each quarter of the year at businesses that operate in one physical location. This paper describes the creation of the data tables and the value they add to the existing Business Dynamics Statistics (BDS) product. We then present some analysis of the published statistics to provide context for the numbers and demonstrate how they can be used to understand both national and local business conditions, with a particular focus on 2020 and the recession induced by the COVID-19 pandemic. We next examine how firms fared in this recession compared to the Great Recession that began in the fourth quarter of 2007. We also consider the heterogenous impact of the pandemic on various industries and areas of the country, showing which types of businesses in which locations were particularly hard hit. We examine business exit rates in some detail and consider why different metro areas experienced the pandemic in different ways. We also consider entry rates and look for evidence of a surge in new businesses as seen in other data sources. We finish by providing a preview of on-going research to match the BDS to worker demographics and show statistics on the relationship between the characteristics of the firm's workers and outcomes such as firm exit and net job creation.
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